Evidence map›Paper›PMID 41557840›Full record

ArticleBioinformatics (Oxford, England)2026

3D dendritic spines shape descriptors for efficient classification and morphology analysis in control and Alzheimer's disease modeling neurons.

Daria Smirnova, Anita Ustinova, Viacheslav Chukanov, Ekaterina Pchitskaya

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Daria SmirnovaLaboratory of Biomedical Imaging and Data Analysis, Institute of Biomedical Systems and Biotechnology, Peter the Great St. Petersburg Polytechnic University, Khlopina St. 11, St. Petersburg, 194021, Russia.ORCID 0009-0001-7035-7449
Anita UstinovaLaboratory of Biomedical Imaging and Data Analysis, Institute of Biomedical Systems and Biotechnology, Peter the Great St. Petersburg Polytechnic University, Khlopina St. 11, St. Petersburg, 194021, Russia.ORCID 0009-0004-5524-3090
Viacheslav ChukanovLaboratory of Biomedical Imaging and Data Analysis, Institute of Biomedical Systems and Biotechnology, Peter the Great St. Petersburg Polytechnic University, Khlopina St. 11, St. Petersburg, 194021, Russia.ORCID 0000-0002-4221-5072
Ekaterina PchitskayaLaboratory of Biomedical Imaging and Data Analysis, Institute of Biomedical Systems and Biotechnology, Peter the Great St. Petersburg Polytechnic University, Khlopina St. 11, St. Petersburg, 194021, Russia.ORCID 0000-0002-3644-6886

Funding

Brain Program of the IDEAS Research CentreMinistry of Science and Higher Education of the Russian Federation FSEG-2024-0025
6 · The paper itself

Abstract

motivationDendritic spines, postsynaptic structures characterized by their complex shapes, provide the essential structural foundation for synaptic function. Their shape is dynamic, undergoing alterations in various conditions, notably during neurodegenerative disorders like Alzheimer's disease. The dramatically increasing prevalence of such diseases highlights an urgent need for effective treatments. A key strategy in developing these treatments involves evaluating how dendritic spine morphology responds to potential therapeutic compounds. Although a link between spine shape and function is recognized, its precise nature is still not fully elucidated. Consequently, advancing our understanding of dendritic spines in both health and disease necessitates the urgent development of more effective methods for assessing their morphology.

resultsThis study introduces qualitatively new 3D dendritic shape descriptors based on spherical harmonics and Zernike moments and proposes a bases on them clustering approach for grouping dendritic spines with similar shapes applied to 3D polygonal spines meshes acquired from Z-stack dendrite images. By integrating these methods, we achieve improved differentiation between normal and pathological spines represented by the Alzheimer's disease in vitro model, offering a more precise representation of morphological diversity. Additionally, the proposed spherical harmonics approach enables dendritic spine reconstruction from vector-based shape representations, providing a novel tool for studying structural changes associated with neurodegeneration and possibilities for synthetic dendritic spines dataset generation. AVAILABILITY AND IMPLEMENTATION: The software used for experiments is public and available at https://github.com/Biomed-imaging-lab/SpineTool with the DOI: 10.5281/zenodo.17359066. Descriptors codebase is available at https://github.com/Biomed-imaging-lab/Spine-Shape-Descriptors with the DOI: 10.5281/zenodo.17302859.

Indexed as

Alzheimer DiseaseDendritic SpinesImaging, Three-DimensionalModels, NeurologicalNeuronsAnimalsHumans

Identifiers

PMID41557840
PMCPMC12891915

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